PK parameters

Source: vignettes/pk-parameters.Rmd 
require(ospsuite.reportingengine)
#> Loading required package: ospsuite.reportingengine
#> Loading required package: ospsuite
#> Warning: package 'ospsuite' was built under R version 4.0.5
#> Loading required package: rClr
#> Warning: package 'rClr' was built under R version 3.6.3
#> Loading the dynamic library for Microsoft .NET runtime...
#> Loaded Common Language Runtime version 4.0.30319.42000
#> Loading required package: tlf
#> Warning: package 'tlf' was built under R version 4.1.0

This vignette focuses on PK parameters plots and tables generated using either mean model or population workflows.

Overview

The PK parameter task aims at generating tables and, when possible, plots of requested PK parameters. Depending on the workflow type, different plots and tables can be obtained.

Inputs

Results obtained from the calculatePKParameters task and stored as csv files within the subdirectory PKAnalysisResults from the workflowFolder directory are required in order to perform the PK parameters (plotPKParameters) task. As a consequence, if the workflow output folder does not include the appropriate simulations, the task calculatePKParameters needs to be active.

The objects SimulationSet (or PopulationSimulationSet for population workflows) and Output define which and how the simulated PK parameters will be reported.

Outputs

Mean model workflow

For mean model workflows, the PK parameters task generates directly the table of PK parameters requested in the requested Output field pkParameters.

Population model workflow

The distribution of the calculated PK parameters for each requested output are plotted as Box Whisker plots along with a table. By default, the 5th, 25th, 50th, 75th and 95th percentiles are plotted.

Different types of a population workflow will lead to different output:

  • Parallel Comparison: PK parameters are plotted parallel in the Box Whisker plots without any reference population.
  • Ratio Comparison: Same as Parallel Comparison with additional Box Whisker plots and tables of the ratio of the PK parameters to the reference population is calculated and plotted in Box Whisker plots.
  • Pediatric: PK Parameters are also plotted as range plots of the PK parameters vs. age and weight.

Regarding range, the task option xParameters can be used to define which demography parameters to use in x-axis of range plots. Parallel and ratio use a NULL value by default, however it is also possible to add range plots to a Parallel or ratio comparison workflow by updating the value of xParameters.

Regarding ratio comparison, the formula below is used to calculate the Box Whisker plot percentile values: \[PK\ Parameter\ Percentile\ Ratio = \frac{PK\ Parameter\ Percentile\ in\ Population}{PK\ Parameter\ Percentile\ in\ Reference\ Population}\]

Template examples

The following sections will introduce template scripts including PK parameter tasks as well as their associated reports. Table 1 shows the features tested in each template script.

Table 1: Features tested by each template script
Example Workflow Workflow Type Simulation Sets
1 Mean model - 1
2 Mean model - 1
3 Mean model - 2
4 Mean model - 2
5 Population model Parallel 1
6 Population model Parallel 2
7 Population model Ratio 2
8 Population model Pediatric 2
9 Population model Ratio 2

Example 1

Example 1 shows a basic example of PK parameters performed by a mean model workflow. In this example, ‘C_max’ and ‘AUC_tEnd’ are calculated. Since the task PK parameters requires results from the folder PKAnalysisResults, the tasks simulate and calculatePKParameters need to be performed. The task simulate needs to be performed because the task calculatePKParameters is using the time profile simulation results.

# Get the pkml simulation file: "MiniModel2.pkml"
simulationFile <- system.file("extdata", "MiniModel2.pkml",
  package = "ospsuite.reportingengine"
)

# Define the input parameters
outputA <- Output$new(
  path = "Organism|A|Concentration in container",
  displayName = "A",
  pkParameters = c("C_max", "AUC_tEnd")
)

setA <- SimulationSet$new(
  simulationSetName = "A",
  simulationFile = simulationFile,
  outputs = outputA
)

# Create the workflow instance
workflow1 <-
  MeanModelWorkflow$new(
    simulationSets = setA,
    workflowFolder = "Example-1"
  )
#>  [1] "14/06/2022 - 08:42:18 : Reporting Engine Information:"           
#>  [2] "14/06/2022 - 08:42:18 : Date: 14/06/2022 - 08:42:18"             
#>  [3] "14/06/2022 - 08:42:18 : User Information:"                       
#>  [4] "14/06/2022 - 08:42:18 : Computer Name: DESKTOP-KJ5K5R4"          
#>  [5] "14/06/2022 - 08:42:18 : User: pchelle"                           
#>  [6] "14/06/2022 - 08:42:18 : Login: pchelle"                          
#>  [7] "14/06/2022 - 08:42:18 : System is NOT validated"                 
#>  [8] "14/06/2022 - 08:42:18 : System versions:"                        
#>  [9] "14/06/2022 - 08:42:18 : R version: R version 3.6.1 (2019-07-05)" 
#> [10] "14/06/2022 - 08:42:18 : OSP Suite Package version: 11.0.81"      
#> [11] "14/06/2022 - 08:42:18 : OSP Reporting Engine version: 2.0.0.9000"
#> [12] "14/06/2022 - 08:42:18 : tlf version: 1.4.78"

# Set the workflow tasks to be run
workflow1$activateTasks(c("simulate", 
                          "calculatePKParameters", 
                          "plotPKParameters"))

# Run the workflow
workflow1$runWorkflow()
#> [1] "14/06/2022 - 08:42:19 : Starting run of mean model workflow"
#> [1] "14/06/2022 - 08:42:19 : Starting Perform simulation task"
#> [1] "14/06/2022 - 08:42:19 : Loading subset 1 of 1"
#> [1] "14/06/2022 - 08:42:19 : Simulating subset 1 of 1"
#> [1] "14/06/2022 - 08:42:19 : Starting Calculate PK parameters task"
#> [1] "14/06/2022 - 08:42:19 : Calculate PK parameters for simulation set A"
#> [1] "14/06/2022 - 08:42:19 : PK parameter calculation completed."
#> [1] "14/06/2022 - 08:42:20 : Starting: Plot PK parameters task"
#> [1] "14/06/2022 - 08:42:20 : Plot PK parameters task for A"
#> Executing: pandoc --self-contained --wrap=none --toc --reference-doc="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/reference.docx" --lua-filter="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/pagebreak.lua" --resource-path="Example-1" -t docx -o "Example-1/Report-word.docx" "Example-1/Report-word.md"

The output report for Example 1 is shown below. The table indicates the requested parameters in their base unit.

Report

1 PK parameters

1.1 PK parameters for A

Table 1-1: PK parameters for A

Path Parameter Value Unit
A C_max 3.85 µmol/l
A AUC_tEnd 4732.24 µmol*min/l



Example 2

Example 2 shows how to perform workflows with user defined PK parameters. In this example, ‘MyAUC’ is added to the list of PK parameters. To create user defined PK parameter, the function addUserDefinedPKParameter() from the ospsuite package needs to be used.

# Get the pkml simulation file: "MiniModel2.pkml"
simulationFile <- system.file("extdata", "MiniModel2.pkml",
  package = "ospsuite.reportingengine"
)

myAUC <- addUserDefinedPKParameter(name = "MyAUC", standardPKParameter = StandardPKParameter$AUC_tEnd)
myAUC$startTime <- 50
myAUC$endTime <- 80

# Define the input parameters
outputA <- Output$new(
  path = "Organism|A|Concentration in container",
  displayName = "A",
  pkParameters = c("C_max", "AUC_tEnd", "MyAUC")
)

setA <- SimulationSet$new(
  simulationSetName = "A",
  simulationFile = simulationFile,
  outputs = outputA
)

# Create the workflow instance
workflow2 <-
  MeanModelWorkflow$new(
    simulationSets = setA,
    workflowFolder = "Example-2"
  )
#>  [1] "14/06/2022 - 08:42:21 : Reporting Engine Information:"           
#>  [2] "14/06/2022 - 08:42:21 : Date: 14/06/2022 - 08:42:21"             
#>  [3] "14/06/2022 - 08:42:21 : User Information:"                       
#>  [4] "14/06/2022 - 08:42:21 : Computer Name: DESKTOP-KJ5K5R4"          
#>  [5] "14/06/2022 - 08:42:21 : User: pchelle"                           
#>  [6] "14/06/2022 - 08:42:21 : Login: pchelle"                          
#>  [7] "14/06/2022 - 08:42:21 : System is NOT validated"                 
#>  [8] "14/06/2022 - 08:42:21 : System versions:"                        
#>  [9] "14/06/2022 - 08:42:21 : R version: R version 3.6.1 (2019-07-05)" 
#> [10] "14/06/2022 - 08:42:21 : OSP Suite Package version: 11.0.81"      
#> [11] "14/06/2022 - 08:42:21 : OSP Reporting Engine version: 2.0.0.9000"
#> [12] "14/06/2022 - 08:42:21 : tlf version: 1.4.78"

# Set the workflow tasks to be run
workflow2$activateTasks(c("simulate", 
                          "calculatePKParameters", 
                          "plotPKParameters"))

# Run the workflow
workflow2$runWorkflow()
#> [1] "14/06/2022 - 08:42:21 : Starting run of mean model workflow"
#> [1] "14/06/2022 - 08:42:21 : Starting Perform simulation task"
#> [1] "14/06/2022 - 08:42:21 : Loading subset 1 of 1"
#> [1] "14/06/2022 - 08:42:21 : Simulating subset 1 of 1"
#> [1] "14/06/2022 - 08:42:21 : Starting Calculate PK parameters task"
#> [1] "14/06/2022 - 08:42:21 : Calculate PK parameters for simulation set A"
#> [1] "14/06/2022 - 08:42:22 : PK parameter calculation completed."
#> [1] "14/06/2022 - 08:42:22 : Starting: Plot PK parameters task"
#> [1] "14/06/2022 - 08:42:22 : Plot PK parameters task for A"
#> Executing: pandoc --self-contained --wrap=none --toc --reference-doc="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/reference.docx" --lua-filter="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/pagebreak.lua" --resource-path="Example-2" -t docx -o "Example-2/Report-word.docx" "Example-2/Report-word.md"

The output report for Example 2 is shown below. The table indicates the requested parameters in their base unit.

Report

1 PK parameters

1.1 PK parameters for A

Table 1-1: PK parameters for A

Path Parameter Value Unit
A C_max 3.85 µmol/l
A AUC_tEnd 4732.24 µmol*min/l
A MyAUC 67.57 µmol*min/l



Example 3

Example 3 shows a similar example with 2 simulation sets. In this example, the names and units of the PK parameters are updated using the function updatePKParameter from the ospsuite package.

# Get the pkml simulation file: "MiniModel2.pkml"
simulationFileA <- system.file("extdata", "MiniModel1.pkml",
  package = "ospsuite.reportingengine"
)

simulationFileB <- system.file("extdata", "MiniModel2.pkml",
  package = "ospsuite.reportingengine"
)

updatePKParameter("C_max", 
                  displayName = "Cmax",
                  displayUnit = "nmol/l")
#> PKParameter: 
#>    Name: C_max 
#>    DisplayName: Cmax 
#>    Dimension: Concentration (molar) 
#>    DisplayUnit: nmol/l
updatePKParameter("AUC_tEnd", 
                  displayName = "AUC",
                  displayUnit = "nmol*min/l")
#> PKParameter: 
#>    Name: AUC_tEnd 
#>    DisplayName: AUC 
#>    Dimension: AUC (molar) 
#>    DisplayUnit: nmol*min/l

# Define the input parameters
outputA <- Output$new(
  path = "Organism|A|Concentration in container",
  displayName = "A",
  pkParameters = c("C_max", "AUC_tEnd")
)
outputB <- Output$new(
  path = "Organism|B|Concentration in container",
  displayName = "B",
  pkParameters = c("C_max", "AUC_tEnd")
)

setA <- SimulationSet$new(
  simulationSetName = "A",
  simulationFile = simulationFileA,
  outputs = outputA
)
setB <- SimulationSet$new(
  simulationSetName = "B",
  simulationFile = simulationFileB,
  outputs = outputB
)

# Create the workflow instance
workflow3 <-
  MeanModelWorkflow$new(
    simulationSets = c(setA, setB),
    workflowFolder = "Example-3"
  )
#>  [1] "14/06/2022 - 08:42:23 : Reporting Engine Information:"           
#>  [2] "14/06/2022 - 08:42:23 : Date: 14/06/2022 - 08:42:23"             
#>  [3] "14/06/2022 - 08:42:23 : User Information:"                       
#>  [4] "14/06/2022 - 08:42:23 : Computer Name: DESKTOP-KJ5K5R4"          
#>  [5] "14/06/2022 - 08:42:23 : User: pchelle"                           
#>  [6] "14/06/2022 - 08:42:23 : Login: pchelle"                          
#>  [7] "14/06/2022 - 08:42:23 : System is NOT validated"                 
#>  [8] "14/06/2022 - 08:42:23 : System versions:"                        
#>  [9] "14/06/2022 - 08:42:23 : R version: R version 3.6.1 (2019-07-05)" 
#> [10] "14/06/2022 - 08:42:23 : OSP Suite Package version: 11.0.81"      
#> [11] "14/06/2022 - 08:42:23 : OSP Reporting Engine version: 2.0.0.9000"
#> [12] "14/06/2022 - 08:42:23 : tlf version: 1.4.78"

# Set the workflow tasks to be run
workflow3$activateTasks(c("simulate", 
                          "calculatePKParameters", 
                          "plotPKParameters"))

# Run the workflow
workflow3$runWorkflow()
#> [1] "14/06/2022 - 08:42:23 : Starting run of mean model workflow"
#> [1] "14/06/2022 - 08:42:23 : Starting Perform simulation task"
#> [1] "14/06/2022 - 08:42:23 : Loading subset 1 of 1"
#> [1] "14/06/2022 - 08:42:23 : Simulating subset 1 of 1"
#> [1] "14/06/2022 - 08:42:23 : Starting Calculate PK parameters task"
#> [1] "14/06/2022 - 08:42:23 : Calculate PK parameters for simulation set A"
#> [1] "14/06/2022 - 08:42:24 : PK parameter calculation completed."
#> [1] "14/06/2022 - 08:42:24 : Calculate PK parameters for simulation set B"
#> [1] "14/06/2022 - 08:42:24 : PK parameter calculation completed."
#> [1] "14/06/2022 - 08:42:24 : Starting: Plot PK parameters task"
#> [1] "14/06/2022 - 08:42:24 : Plot PK parameters task for A"
#> [1] "14/06/2022 - 08:42:24 : Plot PK parameters task for B"
#> Executing: pandoc --self-contained --wrap=none --toc --reference-doc="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/reference.docx" --lua-filter="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/pagebreak.lua" --resource-path="Example-3" -t docx -o "Example-3/Report-word.docx" "Example-3/Report-word.md"

The output report for Example 3 is shown below. The table indicates the requested parameters in the updated units and display names.

Report

1 PK parameters

1.1 PK parameters for A

Table 1-1: PK parameters for A

Path Parameter Value Unit
A Cmax 2850.19 nmol/l
A AUC 3292135.01 nmol*min/l



1.2 PK parameters for B

Table 1-2: PK parameters for B

Path Parameter Value Unit
B Cmax 2000.00 nmol/l
B AUC 1027759.40 nmol*min/l



Example 4

Example 4 shows a different way of updating the PK parameters using the instance PkParameterInfo. Using that instance, it is possible to define different names and units for the same parameters from different paths or simulations.

# Get the pkml simulation file: "MiniModel2.pkml"
simulationFile <- system.file("extdata", "MiniModel2.pkml",
  package = "ospsuite.reportingengine"
)

CmaxA <- PkParameterInfo$new("C_max", 
                  displayName = "Cmax from A",
                  displayUnit = "µmol/l"))
CmaxB <- PkParameterInfo$new("C_max", 
                  displayName = "Cmax from B",
                  displayUnit = "nmol/l")
AUCA <- PkParameterInfo$new("AUC_tEnd", 
                  displayName = "AUC from A",
                  displayUnit = "µmol*min/l"))
AUCB <- PkParameterInfo$new("AUC_tEnd", 
                  displayName = "AUC from B",
                  displayUnit = "nmol*min/l")

# Define the input parameters
outputA <- Output$new(
  path = "Organism|A|Concentration in container",
  displayName = "A",
  pkParameters = c(CmaxA, AUCA)
)
outputB <- Output$new(
  path = "Organism|B|Concentration in container",
  displayName = "B",
  pkParameters = c(CmaxB, AUCB)
)

setA <- SimulationSet$new(
  simulationSetName = "A",
  simulationFile = simulationFile,
  outputs = outputA
)
setB <- SimulationSet$new(
  simulationSetName = "B",
  simulationFile = simulationFile,
  outputs = outputB
)

# Create the workflow instance
workflow4 <-
  MeanModelWorkflow$new(
    simulationSets = c(setA, setB),
    workflowFolder = "Example-3"
  )
#>  [1] "14/06/2022 - 08:42:25 : Reporting Engine Information:"           
#>  [2] "14/06/2022 - 08:42:25 : Date: 14/06/2022 - 08:42:25"             
#>  [3] "14/06/2022 - 08:42:25 : User Information:"                       
#>  [4] "14/06/2022 - 08:42:25 : Computer Name: DESKTOP-KJ5K5R4"          
#>  [5] "14/06/2022 - 08:42:25 : User: pchelle"                           
#>  [6] "14/06/2022 - 08:42:25 : Login: pchelle"                          
#>  [7] "14/06/2022 - 08:42:25 : System is NOT validated"                 
#>  [8] "14/06/2022 - 08:42:25 : System versions:"                        
#>  [9] "14/06/2022 - 08:42:25 : R version: R version 3.6.1 (2019-07-05)" 
#> [10] "14/06/2022 - 08:42:25 : OSP Suite Package version: 11.0.81"      
#> [11] "14/06/2022 - 08:42:25 : OSP Reporting Engine version: 2.0.0.9000"
#> [12] "14/06/2022 - 08:42:25 : tlf version: 1.4.78"

# Set the workflow tasks to be run
workflow4$activateTasks("plotPKParameters")
workflow4$inactivateTasks(c("simulate", "calculatePKParameters"))

# Run the workflow
workflow4$runWorkflow()
#> [1] "14/06/2022 - 08:42:25 : Starting run of mean model workflow"
#> [1] "14/06/2022 - 08:42:25 : Starting: Plot PK parameters task"
#> [1] "14/06/2022 - 08:42:25 : Plot PK parameters task for A"
#> [1] "14/06/2022 - 08:42:26 : Plot PK parameters task for B"
#> Executing: pandoc --self-contained --wrap=none --toc --reference-doc="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/reference.docx" --lua-filter="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/pagebreak.lua" --resource-path="Example-3" -t docx -o "Example-3/Report-word.docx" "Example-3/Report-word.md"

The output report for Example 4 is shown below. The table indicates the requested parameters in the updated units and display names.

Report

1 PK parameters

1.1 PK parameters for A

Table 1-1: PK parameters for A

Path Parameter Value Unit
A Cmax from A 2.85 µmol/l
A AUC from A 3292.14 µmol*min/l



1.2 PK parameters for B

Table 1-2: PK parameters for B

Path Parameter Value Unit
B Cmax from B 2000.00 nmol/l
B AUC from B 1027759.40 nmol*min/l



Example 5

Example 5 shows pk parameters for a population workflow. In this example, only one population-simulation pair is used. Consequently, the workflow type needs to be a parallel comparison (no reference population).

# Get the pkml simulation file: "MiniModel2.pkml"
simulationFile <- system.file("extdata", "MiniModel2.pkml",
  package = "ospsuite.reportingengine"
)
populationFile <- system.file("extdata", "Pop500_p1p2p3.csv",
  package = "ospsuite.reportingengine"
)

updatePKParameter("C_max", 
                  displayName = "Cmax")
#> PKParameter: 
#>    Name: C_max 
#>    DisplayName: Cmax 
#>    Dimension: Concentration (molar) 
#>    DisplayUnit: µmol/ll
updatePKParameter("AUC_tEnd", 
                  displayName = "AUC")
#> PKParameter: 
#>    Name: AUC_tEnd 
#>    DisplayName: AUC 
#>    Dimension: AUC (molar) 
#>    DisplayUnit: µmol*min/ll

# Define the input parameters
outputA <- Output$new(
  path = "Organism|A|Concentration in container",
  displayName = "A",
  pkParameters = c("C_max", "AUC_tEnd")
)

set500 <- PopulationSimulationSet$new(
  simulationSetName = "Population A",
  simulationFile = simulationFile,
  populationFile = populationFile,
  outputs = outputA
)

# Create the workflow instance
workflow5 <-
  PopulationWorkflow$new(
    workflowType = PopulationWorkflowTypes$parallelComparison,
    simulationSets = set500,
    workflowFolder = "Example-5"
  )
#>  [1] "14/06/2022 - 08:42:27 : Reporting Engine Information:"           
#>  [2] "14/06/2022 - 08:42:27 : Date: 14/06/2022 - 08:42:27"             
#>  [3] "14/06/2022 - 08:42:27 : User Information:"                       
#>  [4] "14/06/2022 - 08:42:27 : Computer Name: DESKTOP-KJ5K5R4"          
#>  [5] "14/06/2022 - 08:42:27 : User: pchelle"                           
#>  [6] "14/06/2022 - 08:42:27 : Login: pchelle"                          
#>  [7] "14/06/2022 - 08:42:27 : System is NOT validated"                 
#>  [8] "14/06/2022 - 08:42:27 : System versions:"                        
#>  [9] "14/06/2022 - 08:42:27 : R version: R version 3.6.1 (2019-07-05)" 
#> [10] "14/06/2022 - 08:42:27 : OSP Suite Package version: 11.0.81"      
#> [11] "14/06/2022 - 08:42:27 : OSP Reporting Engine version: 2.0.0.9000"
#> [12] "14/06/2022 - 08:42:27 : tlf version: 1.4.78"

# Set the workflow tasks to be run
workflow5$activateTasks(c("simulate", 
                          "calculatePKParameters", 
                          "plotPKParameters"))

# Run the workflow
workflow5$runWorkflow()
#> [1] "14/06/2022 - 08:42:27 : Starting run of population workflow"
#> [1] "14/06/2022 - 08:42:27 : Starting Perform simulation task"
#> [1] "14/06/2022 - 08:42:27 : Starting Calculate PK parameters task"
#> [1] "14/06/2022 - 08:42:27 : Calculate PK parameters for simulation set Population A"
#> [1] "14/06/2022 - 08:42:28 : PK parameter calculation completed."
#> [1] "14/06/2022 - 08:42:28 : Starting: Plot PK parameters task"
#> Executing: pandoc --self-contained --wrap=none --toc --reference-doc="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/reference.docx" --lua-filter="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/pagebreak.lua" --resource-path="Example-5" -t docx -o "Example-5/Report-word.docx" "Example-5/Report-word.md"

The output report for Example 6 is shown below. For the population, Box Whisker plots in linear and log scales are.

Report

Table of Contents

1 PK parameters

Figure 1-1: Cmax of A for Population A shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in linear scale.



Table 1-1: Cmax of A for Population A reported in [µmol/l]

Population N 5th percentile 25th percentile 50th percentile 75th percentile 95th percentile mean standard deviation geo mean geo standard deviation
Population A 500 2.84 2.86 4.78 6.68 6.73 4.77 1.57 4.50 1.42



Figure 1-2: Cmax of A for Population A shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in logarithmic scale.



Figure 1-3: AUC of A for Population A shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in linear scale.



Table 1-2: AUC of A for Population A reported in [µmol*min/l]

Population N 5th percentile 25th percentile 50th percentile 75th percentile 95th percentile mean standard deviation geo mean geo standard deviation
Population A 500 3264.06 3317.53 5661.77 7980.97 8086.59 5656.20 1934.15 5305.10 1.44



Figure 1-4: AUC of A for Population A shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in logarithmic scale.



Example 6

Example 6 shows pk parameters for a population workflow. In this example, 2 population-simulation pairs are now used. Consequently, the workflow type needs to be a parallel comparison (no reference population).

# Get the pkml simulation file: "MiniModel2.pkml"
simulationFile <- system.file("extdata", "MiniModel2.pkml",
  package = "ospsuite.reportingengine"
)
populationFile500 <- system.file("extdata", "Pop500_p1p2p3.csv",
  package = "ospsuite.reportingengine"
)
populationFile200 <- system.file("extdata", "Pop200_p1p2p3.csv",
  package = "ospsuite.reportingengine"
)

updatePKParameter("C_max", 
                  displayName = "Cmax")
#> PKParameter: 
#>    Name: C_max 
#>    DisplayName: Cmax 
#>    Dimension: Concentration (molar) 
#>    DisplayUnit: µmol/ll
updatePKParameter("AUC_tEnd", 
                  displayName = "AUC")
#> PKParameter: 
#>    Name: AUC_tEnd 
#>    DisplayName: AUC 
#>    Dimension: AUC (molar) 
#>    DisplayUnit: µmol*min/ll

# Define the input parameters
outputA <- Output$new(
  path = "Organism|A|Concentration in container",
  displayName = "A",
  pkParameters = c("C_max", "AUC_tEnd")
)

set500 <- PopulationSimulationSet$new(
  simulationSetName = "500 individuals",
  simulationFile = simulationFile,
  populationFile = populationFile500,
  outputs = outputA
)
set200 <- PopulationSimulationSet$new(
  simulationSetName = "200 individuals",
  simulationFile = simulationFile,
  populationFile = populationFile200,
  outputs = outputA
)

# Create the workflow instance
workflow6 <-
  PopulationWorkflow$new(
    workflowType = PopulationWorkflowTypes$parallelComparison,
    simulationSets = c(set500, set200),
    workflowFolder = "Example-6"
  )
#>  [1] "14/06/2022 - 08:42:32 : Reporting Engine Information:"           
#>  [2] "14/06/2022 - 08:42:32 : Date: 14/06/2022 - 08:42:32"             
#>  [3] "14/06/2022 - 08:42:32 : User Information:"                       
#>  [4] "14/06/2022 - 08:42:32 : Computer Name: DESKTOP-KJ5K5R4"          
#>  [5] "14/06/2022 - 08:42:32 : User: pchelle"                           
#>  [6] "14/06/2022 - 08:42:32 : Login: pchelle"                          
#>  [7] "14/06/2022 - 08:42:32 : System is NOT validated"                 
#>  [8] "14/06/2022 - 08:42:32 : System versions:"                        
#>  [9] "14/06/2022 - 08:42:32 : R version: R version 3.6.1 (2019-07-05)" 
#> [10] "14/06/2022 - 08:42:32 : OSP Suite Package version: 11.0.81"      
#> [11] "14/06/2022 - 08:42:32 : OSP Reporting Engine version: 2.0.0.9000"
#> [12] "14/06/2022 - 08:42:32 : tlf version: 1.4.78"

# Set the workflow tasks to be run
workflow6$activateTasks(c("simulate", 
                          "calculatePKParameters", 
                          "plotPKParameters"))

# Run the workflow
workflow6$runWorkflow()
#> [1] "14/06/2022 - 08:42:32 : Starting run of population workflow"
#> [1] "14/06/2022 - 08:42:32 : Starting Perform simulation task"
#> [1] "14/06/2022 - 08:42:33 : Starting Calculate PK parameters task"
#> [1] "14/06/2022 - 08:42:33 : Calculate PK parameters for simulation set 500 individuals"
#> [1] "14/06/2022 - 08:42:33 : PK parameter calculation completed."
#> [1] "14/06/2022 - 08:42:33 : Calculate PK parameters for simulation set 200 individuals"
#> [1] "14/06/2022 - 08:42:34 : PK parameter calculation completed."
#> [1] "14/06/2022 - 08:42:34 : Starting: Plot PK parameters task"
#> Executing: pandoc --self-contained --wrap=none --toc --reference-doc="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/reference.docx" --lua-filter="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/pagebreak.lua" --resource-path="Example-6" -t docx -o "Example-6/Report-word.docx" "Example-6/Report-word.md"

The output report for Example 6 is shown below. For the population, BoxWhisker plots in linear and log scales are.

Report

Table of Contents

1 PK parameters

Figure 1-1: Cmax of A for 500 individuals, 200 individuals shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in linear scale.



Table 1-1: Cmax of A for 500 individuals, 200 individuals reported in [µmol/l]

Population N 5th percentile 25th percentile 50th percentile 75th percentile 95th percentile mean standard deviation geo mean geo standard deviation
500 individuals 500 2.84 2.86 4.78 6.68 6.73 4.77 1.57 4.50 1.42
200 individuals 200 2.31 2.42 4.06 5.65 5.86 4.05 1.38 3.80 1.44



Figure 1-2: Cmax of A for 500 individuals, 200 individuals shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in logarithmic scale.



Figure 1-3: AUC of A for 500 individuals, 200 individuals shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in linear scale.



Table 1-2: AUC of A for 500 individuals, 200 individuals reported in [µmol*min/l]

Population N 5th percentile 25th percentile 50th percentile 75th percentile 95th percentile mean standard deviation geo mean geo standard deviation
500 individuals 500 3264.06 3317.53 5661.77 7980.97 8086.59 5656.20 1934.15 5305.10 1.44
200 individuals 200 2548.23 2666.13 4642.80 6563.65 6797.81 4630.73 1659.76 4314.38 1.47



Figure 1-4: AUC of A for 500 individuals, 200 individuals shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in logarithmic scale.



Example 7

Example 7 is the same as the previous example, only this time a ratio type population workflow is performed. As a consequence, the workflow type was specified when creating the workflow instance. In this workflow type, a reference population is needed. Since the workflows can re-use the results from the previous PK analysis, only the plot task needs to be re-run.

# Get the pkml simulation file: "MiniModel2.pkml"
simulationFile <- system.file("extdata", "MiniModel2.pkml",
  package = "ospsuite.reportingengine"
)
populationFile500 <- system.file("extdata", "Pop500_p1p2p3.csv",
  package = "ospsuite.reportingengine"
)
populationFile200 <- system.file("extdata", "Pop200_p1p2p3.csv",
  package = "ospsuite.reportingengine"
)

updatePKParameter("C_max", 
                  displayName = "Cmax")
#> PKParameter: 
#>    Name: C_max 
#>    DisplayName: Cmax 
#>    Dimension: Concentration (molar) 
#>    DisplayUnit: µmol/ll
updatePKParameter("AUC_tEnd", 
                  displayName = "AUC")
#> PKParameter: 
#>    Name: AUC_tEnd 
#>    DisplayName: AUC 
#>    Dimension: AUC (molar) 
#>    DisplayUnit: µmol*min/ll

# Define the input parameters
outputA <- Output$new(
  path = "Organism|A|Concentration in container",
  displayName = "A",
  pkParameters = c("C_max", "AUC_tEnd")
)

set500 <- PopulationSimulationSet$new(
  referencePopulation = TRUE,
  simulationSetName = "500 individuals",
  simulationFile = simulationFile,
  populationFile = populationFile500,
  outputs = outputA
)
set200 <- PopulationSimulationSet$new(
  simulationSetName = "200 individuals",
  simulationFile = simulationFile,
  populationFile = populationFile200,
  outputs = outputA
)


# Create the workflow instance
workflow7 <-
  PopulationWorkflow$new(
    workflowType = PopulationWorkflowTypes$ratioComparison,
    simulationSets = c(set500, set200),
    workflowFolder = "Example-6"
  )
#>  [1] "14/06/2022 - 08:42:38 : Reporting Engine Information:"           
#>  [2] "14/06/2022 - 08:42:38 : Date: 14/06/2022 - 08:42:38"             
#>  [3] "14/06/2022 - 08:42:38 : User Information:"                       
#>  [4] "14/06/2022 - 08:42:38 : Computer Name: DESKTOP-KJ5K5R4"          
#>  [5] "14/06/2022 - 08:42:38 : User: pchelle"                           
#>  [6] "14/06/2022 - 08:42:38 : Login: pchelle"                          
#>  [7] "14/06/2022 - 08:42:38 : System is NOT validated"                 
#>  [8] "14/06/2022 - 08:42:38 : System versions:"                        
#>  [9] "14/06/2022 - 08:42:38 : R version: R version 3.6.1 (2019-07-05)" 
#> [10] "14/06/2022 - 08:42:38 : OSP Suite Package version: 11.0.81"      
#> [11] "14/06/2022 - 08:42:38 : OSP Reporting Engine version: 2.0.0.9000"
#> [12] "14/06/2022 - 08:42:38 : tlf version: 1.4.78"

# Set the workflow tasks to be run
workflow7$inactivateTasks()
workflow7$activateTasks("plotPKParameters")

# Run the workflow
workflow7$runWorkflow()
#> [1] "14/06/2022 - 08:42:38 : Starting run of population workflow"
#> [1] "14/06/2022 - 08:42:38 : Starting: Plot PK parameters task"
#> Warning in dir.create(file.path(self$workflowFolder, self$outputFolder)):
#> 'Example-6\PKAnalysis' already exists
#> Executing: pandoc --self-contained --wrap=none --toc --reference-doc="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/reference.docx" --lua-filter="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/pagebreak.lua" --resource-path="Example-6" -t docx -o "Example-6/Report-word.docx" "Example-6/Report-word.md"

The output report for Example 7 is shown below.

Report

Table of Contents

1 PK parameters

Figure 1-1: Cmax of A for 500 individuals, 200 individuals shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in linear scale.



Table 1-1: Cmax of A for 500 individuals, 200 individuals reported in [µmol/l]

Population N 5th percentile 25th percentile 50th percentile 75th percentile 95th percentile mean standard deviation geo mean geo standard deviation
500 individuals 500 2.84 2.86 4.78 6.68 6.73 4.77 1.57 4.50 1.42
200 individuals 200 2.31 2.42 4.06 5.65 5.86 4.05 1.38 3.80 1.44



Figure 1-2: Cmax of A for 500 individuals, 200 individuals shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in logarithmic scale.



Figure 1-3: Cmax of A for 200 individuals in comparison to 500 individuals shown as box-whisker plot, which indicates ratios of the 5th, 25th, 50th, 75th, and 95th percentiles in linear scale.



Table 1-2: Ratios of Cmax of A for 200 individuals in comparison to 500 individuals

Population N 5th percentile 25th percentile 50th percentile 75th percentile 95th percentile mean standard deviation geo mean geo standard deviation
200 individuals 200 0.81 0.84 0.85 0.85 0.87 0.85 0.88 0.84 1.01



Figure 1-4: Cmax of A for 200 individuals in comparison to 500 individuals shown as box-whisker plot, which indicates ratios of the 5th, 25th, 50th, 75th, and 95th percentiles in logarithmic scale.



Figure 1-5: AUC of A for 500 individuals, 200 individuals shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in linear scale.



Table 1-3: AUC of A for 500 individuals, 200 individuals reported in [µmol*min/l]

Population N 5th percentile 25th percentile 50th percentile 75th percentile 95th percentile mean standard deviation geo mean geo standard deviation
500 individuals 500 3264.06 3317.53 5661.77 7980.97 8086.59 5656.20 1934.15 5305.10 1.44
200 individuals 200 2548.23 2666.13 4642.80 6563.65 6797.81 4630.73 1659.76 4314.38 1.47



Figure 1-6: AUC of A for 500 individuals, 200 individuals shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in logarithmic scale.



Figure 1-7: AUC of A for 200 individuals in comparison to 500 individuals shown as box-whisker plot, which indicates ratios of the 5th, 25th, 50th, 75th, and 95th percentiles in linear scale.



Table 1-4: Ratios of AUC of A for 200 individuals in comparison to 500 individuals

Population N 5th percentile 25th percentile 50th percentile 75th percentile 95th percentile mean standard deviation geo mean geo standard deviation
200 individuals 200 0.78 0.80 0.82 0.82 0.84 0.82 0.86 0.81 1.02



Figure 1-8: AUC of A for 200 individuals in comparison to 500 individuals shown as box-whisker plot, which indicates ratios of the 5th, 25th, 50th, 75th, and 95th percentiles in logarithmic scale.



Example 8

Example 8 is the same as the 2 previous examples, this time a pediatric type population workflow is performed. As a consequence, the workflow type was specified when creating the workflow instance. In this workflow type, a reference population is needed. In the test population, age and body weight are not population parameters only p1, p2 and p3, as a consequence, p3 was used in the x-axis in the range plots. These parameters will be used instead of age for the range plots. Since the workflows can re-use the results from the previous PK analysis, only the plot task needs to be re-run.

# Get the pkml simulation file: "MiniModel2.pkml"
simulationFile <- system.file("extdata", "MiniModel2.pkml",
  package = "ospsuite.reportingengine"
)
populationFile500 <- system.file("extdata", "Pop500_p1p2p3.csv",
  package = "ospsuite.reportingengine"
)
populationFile200 <- system.file("extdata", "Pop200_p1p2p3.csv",
  package = "ospsuite.reportingengine"
)

updatePKParameter("C_max", 
                  displayName = "Cmax")
#> PKParameter: 
#>    Name: C_max 
#>    DisplayName: Cmax 
#>    Dimension: Concentration (molar) 
#>    DisplayUnit: µmol/ll
updatePKParameter("AUC_tEnd", 
                  displayName = "AUC")
#> PKParameter: 
#>    Name: AUC_tEnd 
#>    DisplayName: AUC 
#>    Dimension: AUC (molar) 
#>    DisplayUnit: µmol*min/ll

# Define the input parameters
outputA <- Output$new(
  path = "Organism|A|Concentration in container",
  displayName = "A",
  pkParameters = c("C_max", "AUC_tEnd")
)

set500 <- PopulationSimulationSet$new(
  referencePopulation = TRUE,
  simulationSetName = "500 individuals",
  simulationFile = simulationFile,
  populationFile = populationFile500,
  outputs = outputA
)
set200 <- PopulationSimulationSet$new(
  simulationSetName = "200 individuals",
  simulationFile = simulationFile,
  populationFile = populationFile200,
  outputs = outputA
)


# Create the workflow instance
workflow8 <-
  PopulationWorkflow$new(
    workflowType = PopulationWorkflowTypes$pediatric,
    simulationSets = c(set500, set200),
    workflowFolder = "Example-6"
  )
#>  [1] "14/06/2022 - 08:42:44 : Reporting Engine Information:"           
#>  [2] "14/06/2022 - 08:42:44 : Date: 14/06/2022 - 08:42:44"             
#>  [3] "14/06/2022 - 08:42:44 : User Information:"                       
#>  [4] "14/06/2022 - 08:42:44 : Computer Name: DESKTOP-KJ5K5R4"          
#>  [5] "14/06/2022 - 08:42:44 : User: pchelle"                           
#>  [6] "14/06/2022 - 08:42:44 : Login: pchelle"                          
#>  [7] "14/06/2022 - 08:42:44 : System is NOT validated"                 
#>  [8] "14/06/2022 - 08:42:44 : System versions:"                        
#>  [9] "14/06/2022 - 08:42:44 : R version: R version 3.6.1 (2019-07-05)" 
#> [10] "14/06/2022 - 08:42:44 : OSP Suite Package version: 11.0.81"      
#> [11] "14/06/2022 - 08:42:44 : OSP Reporting Engine version: 2.0.0.9000"
#> [12] "14/06/2022 - 08:42:44 : tlf version: 1.4.78"

# Set the workflow tasks to be run
workflow8$inactivateTasks()
workflow8$activateTasks("plotPKParameters")

setXParametersForPkParametersPlot(workflow8, "Organism|p3")

# Run the workflow
workflow8$runWorkflow()
#> [1] "14/06/2022 - 08:42:44 : Starting run of population workflow"
#> [1] "14/06/2022 - 08:42:44 : Starting: Plot PK parameters task"
#> Warning in dir.create(file.path(self$workflowFolder, self$outputFolder)):
#> 'Example-6\PKAnalysis' already exists
#> Executing: pandoc --self-contained --wrap=none --toc --reference-doc="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/reference.docx" --lua-filter="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/pagebreak.lua" --resource-path="Example-6" -t docx -o "Example-6/Report-word.docx" "Example-6/Report-word.md"

The output report for Example 8 is shown below.

Report

Table of Contents

1 PK parameters

Figure 1-1: Cmax of A for 500 individuals, 200 individuals shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in linear scale.



Table 1-1: Cmax of A for 500 individuals, 200 individuals reported in [µmol/l]

Population N 5th percentile 25th percentile 50th percentile 75th percentile 95th percentile mean standard deviation geo mean geo standard deviation
500 individuals 500 2.84 2.86 4.78 6.68 6.73 4.77 1.57 4.50 1.42
200 individuals 200 2.31 2.42 4.06 5.65 5.86 4.05 1.38 3.80 1.44



Figure 1-2: Cmax of A for 500 individuals, 200 individuals shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in logarithmic scale.



Figure 1-3: p3-dependence of Cmax for 200 individuals in comparison to 500 individuals. Profiles are plotted in a linear scale.



Figure 1-4: p3-dependence of Cmax for 200 individuals in comparison to 500 individuals. Profiles are plotted in a logarithmic scale.



Figure 1-5: p3-dependence of Cmax for 500 individuals. Profiles are plotted in a linear scale.



Figure 1-6: p3-dependence of Cmax for 500 individuals. Profiles are plotted in a logarithmic scale.



Figure 1-7: p3-dependence of Cmax for 200 individuals. Profiles are plotted in a linear scale.



Figure 1-8: p3-dependence of Cmax for 200 individuals. Profiles are plotted in a logarithmic scale.



Figure 1-9: AUC of A for 500 individuals, 200 individuals shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in linear scale.



Table 1-2: AUC of A for 500 individuals, 200 individuals reported in [µmol*min/l]

Population N 5th percentile 25th percentile 50th percentile 75th percentile 95th percentile mean standard deviation geo mean geo standard deviation
500 individuals 500 3264.06 3317.53 5661.77 7980.97 8086.59 5656.20 1934.15 5305.10 1.44
200 individuals 200 2548.23 2666.13 4642.80 6563.65 6797.81 4630.73 1659.76 4314.38 1.47



Figure 1-10: AUC of A for 500 individuals, 200 individuals shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in logarithmic scale.



Figure 1-11: p3-dependence of AUC for 200 individuals in comparison to 500 individuals. Profiles are plotted in a linear scale.



Figure 1-12: p3-dependence of AUC for 200 individuals in comparison to 500 individuals. Profiles are plotted in a logarithmic scale.



Figure 1-13: p3-dependence of AUC for 500 individuals. Profiles are plotted in a linear scale.



Figure 1-14: p3-dependence of AUC for 500 individuals. Profiles are plotted in a logarithmic scale.



Figure 1-15: p3-dependence of AUC for 200 individuals. Profiles are plotted in a linear scale.



Figure 1-16: p3-dependence of AUC for 200 individuals. Profiles are plotted in a logarithmic scale.



Example 9

In some cases, it may be necessary to compare different PK parameters from different simulations. Example 9 shows how to define a group identifier in PkParameterInfo objects. In this example, a user defined PK parameter named "MyAUC" is created using the function addUserDefinedPKParameter. Then, 2 PkParameterInfo objects are created with the same value for their field group while their field pkParameter indicates a different PK parameter. If group is left undefined, it will take the value defined in pkParameter input argument by default.

The workflow created using this method will compare “AUC_tEnd” from the first population with “myAUC” from the second population.

# Get the pkml simulation file: "MiniModel2.pkml"
simulationFile <- system.file("extdata", "MiniModel2.pkml",
  package = "ospsuite.reportingengine"
)
populationFile500 <- system.file("extdata", "Pop500_p1p2p3.csv",
  package = "ospsuite.reportingengine"
)
populationFile200 <- system.file("extdata", "Pop200_p1p2p3.csv",
  package = "ospsuite.reportingengine"
)

# Create a user defifened PK parameter
myAUC <- addUserDefinedPKParameter(name = "MyAUC", standardPKParameter = StandardPKParameter$AUC_tEnd)
myAUC$startTime <- 50
myAUC$endTime <- 80

# Define PkParameterInfo objects sharing the same groups
AUC500 <- PkParameterInfo$new(
  pkParameter = "AUC_tEnd", 
  group = 1,
  displayName = "AUC",
  displayUnit = "µmol*min/l"))
AUC200 <- PkParameterInfo$new(
  pkParameter = "MyAUC", 
  group = 1,
  displayName = "AUC",
  displayUnit = "µmol*min/l"))

# Define the input parameters
output500 <- Output$new(
  path = "Organism|A|Concentration in container",
  displayName = "A",
  pkParameters = AUC500
)
output200 <- Output$new(
  path = "Organism|A|Concentration in container",
  displayName = "A",
  pkParameters = AUC200
)

set500 <- PopulationSimulationSet$new(
  referencePopulation = TRUE,
  simulationSetName = "500 individuals",
  simulationFile = simulationFile,
  populationFile = populationFile500,
  outputs = output500
)
set200 <- PopulationSimulationSet$new(
  simulationSetName = "200 individuals",
  simulationFile = simulationFile,
  populationFile = populationFile200,
  outputs = output200
)

# Create the workflow instance
workflow9 <-
  PopulationWorkflow$new(
    workflowType = PopulationWorkflowTypes$ratioComparison,
    simulationSets = c(set500, set200),
    workflowFolder = "Example-9"
  )
#>  [1] "14/06/2022 - 08:42:56 : Reporting Engine Information:"           
#>  [2] "14/06/2022 - 08:42:56 : Date: 14/06/2022 - 08:42:56"             
#>  [3] "14/06/2022 - 08:42:56 : User Information:"                       
#>  [4] "14/06/2022 - 08:42:56 : Computer Name: DESKTOP-KJ5K5R4"          
#>  [5] "14/06/2022 - 08:42:56 : User: pchelle"                           
#>  [6] "14/06/2022 - 08:42:56 : Login: pchelle"                          
#>  [7] "14/06/2022 - 08:42:56 : System is NOT validated"                 
#>  [8] "14/06/2022 - 08:42:56 : System versions:"                        
#>  [9] "14/06/2022 - 08:42:56 : R version: R version 3.6.1 (2019-07-05)" 
#> [10] "14/06/2022 - 08:42:56 : OSP Suite Package version: 11.0.81"      
#> [11] "14/06/2022 - 08:42:56 : OSP Reporting Engine version: 2.0.0.9000"
#> [12] "14/06/2022 - 08:42:56 : tlf version: 1.4.78"

# Set the workflow tasks to be run
workflow9$activateTasks(c("simulate", "calculatePKParameters", "plotPKParameters"))
workflow9$activateTasks("plotPKParameters")

# Run the workflow
workflow9$runWorkflow()
#> [1] "14/06/2022 - 08:42:56 : Starting run of population workflow"
#> [1] "14/06/2022 - 08:42:56 : Starting Perform simulation task"
#> [1] "14/06/2022 - 08:42:57 : Starting Calculate PK parameters task"
#> [1] "14/06/2022 - 08:42:57 : Calculate PK parameters for simulation set 500 individuals"
#> [1] "14/06/2022 - 08:42:57 : PK parameter calculation completed."
#> [1] "14/06/2022 - 08:42:57 : Calculate PK parameters for simulation set 200 individuals"
#> [1] "14/06/2022 - 08:42:57 : PK parameter calculation completed."
#> [1] "14/06/2022 - 08:42:57 : Starting: Plot PK parameters task"
#> Executing: pandoc --self-contained --wrap=none --toc --reference-doc="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/reference.docx" --lua-filter="C:/Users/pchelle/AppData/Local/Temp/Rtmpu08iZv/temp_libpath37ac51e3106f/ospsuite.reportingengine/extdata/pagebreak.lua" --resource-path="Example-9" -t docx -o "Example-9/Report-word.docx" "Example-9/Report-word.md"

The output report for Example 9 is shown below.

Report

Table of Contents

1 PK parameters

Figure 1-1: AUC of A for 500 individuals, 200 individuals shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in linear scale.



Table 1-1: AUC of A for 500 individuals, 200 individuals reported in [µmol*min/l]

Population N 5th percentile 25th percentile 50th percentile 75th percentile 95th percentile mean standard deviation geo mean geo standard deviation
500 individuals 500 3264.06 3317.53 5661.77 7980.97 8086.59 5656.20 1934.15 5305.10 1.44
200 individuals 200 33.23 33.73 65.26 96.57 97.57 65.11 25.96 59.50 1.55



Figure 1-2: AUC of A for 500 individuals, 200 individuals shown as box-whisker plot, which indicates the 5th, 25th, 50th, 75th, and 95th percentiles in logarithmic scale.



Figure 1-3: AUC of A for 200 individuals in comparison to 500 individuals shown as box-whisker plot, which indicates ratios of the 5th, 25th, 50th, 75th, and 95th percentiles in linear scale.



Table 1-2: Ratios of AUC of A for 200 individuals in comparison to 500 individuals

Population N 5th percentile 25th percentile 50th percentile 75th percentile 95th percentile mean standard deviation geo mean geo standard deviation
200 individuals 200 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 1.08



Figure 1-4: AUC of A for 200 individuals in comparison to 500 individuals shown as box-whisker plot, which indicates ratios of the 5th, 25th, 50th, 75th, and 95th percentiles in logarithmic scale.